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CC3 -- Why I believe that a third option is needed for climate change
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Retr0gamer wrote: »You see that's a good example of cherry picking due to bias.
You see temperatures adjusted by 1 degree Celsius.
What I see is a trend that shows an increase in temperature of about 1.5 degrees Celsius
Lets make this really easy for you...
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Gaoth Laidir wrote: »Nope, try to keep up. Akrasia has stated that the recent warming has been more than 100% anthropogenic, whatever that means. Absolute nonsense.
It's not absolute nonsense
If you're running on a treadmill you can be running at 15mph while not actually moving anywhere. This is because you're forward momentum from running is balanced out by the conveyor belt in the treadmill going in the opposite direction
If we are in a period of weak solar output (there's another thread on this forum full of people saying we should be in an ice age by now) then the earth is in a naturally cooling phase, and the fact that the earth is warming despite this, means that humans have contributed all of the observed warming plus the extra energy to offset the natural cooling.
If you think that is 'absolute nonsense' then you're not as clever as you think you are0 -
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Instead of asking on here why the raw data gets adjusted and homogenised, it's much better to ask the people who actually do the data processing.
In this case, it's the GHCN v4 which is run by NOAA and they supply the data for GISStemp who then go on to do some more processing and interpolation
Here is what the GCHN have to say about why temperature records are adjusted
https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-monthly-version-4Nearly all weather stations undergo changes in the circumstances under which measurements are taken at some point during their history. For example, thermometers require periodic replacement or recalibration and measurement technology has evolved over time. Temperature recording protocols have also changed at many locations from recording temperatures at fixed hours during the day to once-per-day readings of the 24-hour maximum and minimum. “Fixed” land stations are sometime relocated and even minor temperature equipment moves can change the microclimate exposure of the instruments. In other cases, the land use or land cover in the vicinity of an observing site can change over time, which can impact the local environment that instruments are sampling even when measurement practice is stable. All of the these different modifications to the circumstances of recording near surface air temperature can cause systematic shifts in temperature readings from a station that are unrelated to any real variation in local weather and climate. Moreover, the magnitude of these shifts (or “inhomogeneities”) can be large relative to true climate variability. Inhomogeneities can therefore lead to large systematic errors in the computation of climate trends and variability not only for individual station records, but also in spatial averages.
For this reason, detecting and accounting for artifacts associated with changes in observing practice is an important and necessary endeavor in building climate datasets. In GHCNm v4, shifts in monthly temperature series are detected through automated pairwise comparisons of the station series using the algorithm described in Menne and Williams (2009). This procedure, known as the Pairwise Homogenization Algorithm (PHA), systematically evaluates each time series of monthly average surface air temperature to identify cases in which there is an abrupt shift in one station’s temperature series (the “target” series) relative to many other correlated series from other stations in the region (the “reference” series). The algorithm seeks to resolve the timing of shifts for all station series before computing an adjustment factor to compensate for any one particular shift. These adjustment factors are based on the average change in the magnitude of monthly temperature differences between the target station series with the apparent shift and the reference series with no apparent concurrent shifts.
The PHA has undergone extensive evaluation (e.g., Williams et al. 2012) and GHCNm v4 data are provided as both homogenized (adjusted) and unhomogenized (unadjusted). The homogenized data are known by the string "qcf" and the unhomogenized data are designated by the string "qcu". As described in Menne et al. (2018), the PHA is periodically run as an ensemble to quantify the uncertainty of homogenization. Other components of uncertainty are also evaluated. The combined effect of uncertainties for GHCNm v4 are shown in the figure below.
The paper by Menne et al is here in full for free. https://journals.ametsoc.org/doi/10.1175/JCLI-D-18-0094.1
It describes the methodology for adjusting the data, the validation protocols, the validation of algoryihms used to homogenise the data, the justification for doing so etc
Every month every record is checked using the validation checks in this table
If an observation fails a quality-control check, the value is accompanied by QC flag (flag 2) that indicates what check was failed. A value with no quality-control flag indicates that the datum passed all checks applied or was unable to be tested because of insufficient data. As described in Lawrimore et al. (2011), the quality-control process was designed so that each check has a low false-positive rate, that is, a low probability that valid observations are flagged as errors according to the evaluation approach outlined in Durre et al. (2008). Nevertheless, a QC flag can be overridden if later found by expert assessment to be inaccurate. Any overrides, and the associated justification for making the correction, are documented in NCEI’s Datzilla system (NOAA 2007).
and if the data can not be validated, or if an error is discovered, it is either adjusted, or it is excluded from the record for that month.
What sceptics like to do is 'anomaly hunt'. They look for things that 'don't quite fit' and use this to discard the bulk of the data that they don't want to trust. In reality, any temperature series as big and complex as the GHCN is going to be full of anomalies and errors in data recording and transmission, so a lot of the work in creating these temperature reanalysis products, is in validating the data
And in case one of you comes along and says that these validations are not justified or they are there just to falsify the record, the validations are audited independently through multiple stages and there have been multiple independent studies looking at the algorithms and techniques to ensure that they do not introduce any bias and that their methods are scientifically sound.
Instead of posting youtube videos of disgruntled former employees being interviewed by Breitbart 'journalists' ranting about plots to fake data, you need to provide peer reviewed papers where scientists have demonstrated a flaw in the methods or in some other aspect of the temperature reconstruction that means we should no longer trust it.
What real science does is check for anomalies and try to deal with them in a scientifically justified way while still retaining the information in the dataset.
Certain climate change 'Sceptics' would have a backyard drain filled with the rotting corpses of all the babies they threw out with the bathwater.0 -
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Akrasia, thank you for posting what I've already read and going to the trouble of typing all that long post, however it explains not one iota about what was done to the Valentia data. A nice diversionary tactic, akin to fillibustering, but it didn't answer the question that was asked. Gamer said there were papers out there on what I was asking, so maybe he'll be good enough to provide them. Mindgame still mustn't have time yet.
Here is the Met Éireann quality-controlled dataset again, which shows no such jumps in readings. If this was a station in my back garden then I'd accept it, but this is the flagship station of a national agency, in postitoin for 128 years, as far away from urban inhomogenities as can be. Are you saying Met Éireann are wrong?
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No, they’re measuring different things. GHCN is measuring global average temperatures. Met Eireann do not need to homogeneous Valentia data to match global measurement standardsGaoth Laidir wrote: »Akrasia, thank you for posting what I've already read and going to the trouble of typing all that long post, however it explains not one iota about what was done to the Valentia data. A nice diversionary tactic, akin to fillibustering, but it didn't answer the question that was asked. Gamer said there were papers out there on what I was asking, so maybe he'll be good enough to provide them. Mindgame still mustn't have time yet.
Here is the Met Éireann quality-controlled dataset again, which shows no such jumps in readings. If this was a station in my back garden then I'd accept it, but this is the flagship station of a national agency, in postitoin for 128 years, as far away from urban inhomgenities as can be. Are you saying Met Éireann are wrong?0 -
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I don't get it. On the KNMI climate explorer, the GHCN v3 has a similar graph to that of Met Éireann's.
http://climexp.knmi.nl/gettemp.cgi?WMO=3953&STATION=VALENTIA_OBSE
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